Institute for Robotics and Intelligent Machines (IRIM)

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Launched on November 4, 2013, the Institute for Robotics and Intelligent Machines (IRIM) evolved into Georgia Tech’s newest Interdisciplinary Research Institute (IRI), built upon the foundational work developed in the former Robotics & Intelligent Machines Center (RIM@Georgia Tech). IRIM brings together robotics researchers from across campus—spanning colleges, departments and individual labs—to create new collaborative opportunities for faculty, strengthen partnerships with industry and government and maximize the societal impact of the transformative robotics research conducted at Georgia Tech.

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Recent Submissions

This talk will discuss how to learn representation for
perception and action without using any manual supervision.
Gupta will discuss how we can learn ConvNets for
vision in a completely unsupervised manner using ...

This paper presents two approaches to externally
influence a team of robots by means of time-varying
density functions. These density functions represent rough
references for where the robots should be located. ...

This paper explores how haptic interfaces should
be designed to enable effective human-swarm interactions.
When a single operator is interacting with a team of mobile
robots, there are certain properties of the team ...

In this paper, we investigate the effect of insufficient time-scale separation between inner and the outer loops in a cascaded, networked system under multiple clients. Inspired by the AQM (inner loop) and TCP (outer loop) ...

In this paper, we seek to establish formal guarantees
for whether or not a given human-swarm interaction
(HSI) is appropriate for achieving multi-robot tasks. Examples
of such tasks include guiding a swarm of robots ...

We present an optimization framework that solves
constrained multi-agent optimization problems while keeping
each agent’s state differentially private. The agents in
the network seek to optimize a local objective function ...

Humanoid robots walking across intermittent terrain, robotic arms grasping multifaceted objects, or UAVs
darting left or right around a tree — many of the dynamics and control problems we face today have an
inherently ...

Biological systems are able to move with great elegance, agility, and efficiency in a wide range of environments. Endowing machines with similar capabilities requires designing controllers that can address the challenges ...

Current multi-agent robotic testbeds are prohibitively
expensive or highly specialized and as such their use
is limited to a small number of research laboratories. Given
the high price tag, what is needed to scale ...

The ongoing transformation of air traffic control
towards decentralized decision making based on
ADS-B information shared by neighboring traffic
will allow all aircraft and UAS in particular, to
automatically detect ...

This paper presents an algorithm that optimally
explores an unknown environment with regions of varying
degrees of importance. The algorithm, termed
Ergodic Environmental Exploration (E³), is a finite ...

Robot teaming is a well-studied area, but little
research to date has been conducted on the fundamental
benefits of heterogeneous teams and virtually none on temporal
heterogeneity, where timescales of the various ...

We present and analyze a hybrid computational
architecture for performing multi-agent optimization. The optimization
problems under consideration have convex objective
and constraint functions with mild smoothness ...

This paper considers the problem of controlling a team of heterogeneous agents to conform to high-
level interaction (coordination, sensing, and communication) missions. We consider interactions that can be specified via ...

This paper describes a vision-based control architecture designed to enable autonomous landing on a moving platform.
The landing trajectory is generated by using the receding-horizon differential dynamic programming (DDP), ...

This paper describes the target detection and tracking
architecture used by the Georgia Tech Aerial Robotics team for
the American Helicopter Society (AHS) Micro Aerial Vehicle
(MAV) challenge. The vision system described ...

Given a stream of multimodal sensory data, an autonomous robot must continuously refine its understanding of itself and its environment as it makes decisions on how to act to achieve a goal. These are difficult problems ...